Digital transformation and analytics are nearly inseparable. “At the core of any successful digital transformation is the ability to leverage the company’s data assets to drive superior customer experiences, products and services as well as operating model efficiencies,” says Scott Snyder, a Digital and Innovation Partner with Heidrick & Struggles, and co-author of “Goliath’s Revenge: How Established Companies Turn the Tables on Digital Disruptors.”
Companies typically need data science know-how in order to connect data to analytics or algorithms and deliver digital insight. “Without a critical mass of these data science and analytics skills, companies will struggle to keep up with both customer expectations and new innovation opportunities,” Snyder says.
The gap between supply of and demand for data sciences skills is a problem IT leaders know well. One the one hand, data is growing at an exponential rate. “It’s widely reported that 90 percent of the world’s data has been generated in the last two years, and with data doubling every 1.2 years on average versus processing speed only doubling every one to 1.5 years, companies must become more efficient at analyzing data to keep up,” says Snyder.
[ Is your digital strategy up to date? Read also: 8 digital transformation trends for 2020. ]
On the human resources side, the role of data scientist has become one of the hottest – and hardest to hire for – jobs in the United States. Data scientists were the second-highest paying job in the AI realm for 2019, according to job-posting site Indeed, earning an average salary of $126.927. And the McKinsey Global Institute predicts a shortage of 250,000 data scientists over the next decade in the U.S.
6 steps to develop data skills across your organization
One way to sidestep the skills shortage: Democratize data science across the workforce. Companies can do that a number of ways, for example by upgrading their technology infrastructure and applications to enable straightforward and secure access to data assets and offering data science training to interested and capable employees.
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Snyder offers a number of other actions IT leaders can take to meet their data science needs in more creative ways.
1. Create a solid foundation for democratized data
“It is critical that IT leaders help drive the data advantage for their organizations by putting in place technology infrastructure that makes it easy for any employee or function to access the company’s data assets and data analytics tools and services while maintaining security and protecting production systems,” Snyder says.
On a human level, your emphasis and outreach should reach across the entire organization. “A long-term investment in data literacy across the entire organization can get you farther, faster than pointing a select group of experts at data questions,” Heidi Lanford, Vice President of Enterprise Data & Analytics at Red Hat, recently noted. “It won’t happen overnight, but it will last, resulting in a pool of skilled data users in every function of your business.” To learn about the three-prong data literacy approach she and Red Hat CIO Mike Kelly used, read also: How to create data literacy: 3 keys.
2. Take advantage of new user-friendly platforms
“Automated tools and software will begin to democratize data science skills across the workforce,” says Snyder noting that options like Google’s Cloud AutoML and H2O.ai are increasing access to AI-driven analytics and creating more citizen data scientists. “By allowing smart business analysts to build basic machines learning models without being a programmer, companies can begin to unleash the power of data and automation across their business without being dependent on just an exclusive group of experts.”
Some companies are also experimenting with Robotic Process Automation (RPA) tools for this reason, to open automation opportunities up to people who are not traditional coders.
3. Partner with data science powerhouses
Work with outside technology firms familiar with the latest analytics and AI platforms that have experience enabling greater usage of these powerful platforms. These partners can share best practices on training and education options.
4. Identify would-be data gurus
Identify employees with strong critical thinking and statistics backgrounds who aren’t necessarily programmers or data engineers, widening the pool of possible candidates. Then offer the necessary training to jump-start their data science careers. Snyder is a fan of micro- or nano-degree programs that will allow more employees to learn and apply analytics and ML tools to new user cases.
5. Walk the walk
“The IT group can also set an example by up-skilling its own employees on data analytics skills and publishing success cases of applying data and AI to create innovative solutions for the enterprise,” Snyder says. “This may require attracting some external data science experts into the organization to define new approaches to building data analytics capabilities across the firm if the organization is relatively immature in this area.”
6. Lead the leaders
While the goal is to democratize data access in the larger organization, increasing the data science understanding among the organization’s executive and management teams is also important.
“IT leaders should work to raise the awareness and data literacy of other leaders in the enterprise so they can better enable their teams to create data advantages in every part of the enterprise and drive wholesale transformation,” Snyder says.
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